Praktikum: Data Analytics for Cyber-Physical Systems: Automatic Failure Diagnosis (IN0012, IN2106, IN4285)
Lehrveranstaltung 0000003018 im WS 2020/1
Basisdaten
LV-Art | Praktikum |
---|---|
Umfang | 2 SWS |
betreuende Organisation | Informatik 4 - Lehrstuhl für Software & Systems Engineering (Prof. Pretschner) |
Dozent(inn)en |
Stephan Lipp Ehsan Zibaei Leitung/Koordination: Alexander Pretschner |
Termine |
Mi, 15:00–17:00, MI 01.09.014 |
Zuordnung zu Modulen
-
IN2106: Master-Praktikum / Advanced Practical Course
Dieses Modul ist in den folgenden Katalogen enthalten:- weitere Module aus anderen Fachrichtungen
weitere Informationen
Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).
ergänzende Hinweise | As robotic systems are increasingly utilized in different applications, more and more safety incidents occur during their operation. One way to improve the safety of robotic systems is to look into the vast amount of data produced by them and automatically learn the causes of the incidents. This way the next versions of the robotic system will be designed safer against similar faults. Causal discovery algorithms are powerful tools that use statistical dependency between variables to infer the causal structure and hence reveal the causes of the system failure. The focus of this practical course is on using causal inference methods to discover the causal relationship between events in the context of robotic systems in general and Unmanned Aerial Vehicles as a specific use case. |
---|---|
Links |
E-Learning-Kurs (z. B. Moodle) TUMonline-Eintrag |